# Paper Reading: Embedding Words in Non-Vector Space with Unsupervised Graph Learning

venue: EMNLP 2020 This paper proposes to use a graph-based method to train word embeddings. The graph method is PRODIGE which learns a representation of data in a form of a weighted graph G(V,E,w,p). Each edge has a weight and a Bernoulli random variable indicating whether an edge is present or not. The distance between two nodes is formulated as the expected shortest path distance: … Continue reading Paper Reading: Embedding Words in Non-Vector Space with Unsupervised Graph Learning